Enforcing convexity for improved alignment with constrained local models

Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/tracking in comparison to leading holistic approaches (e.g., AAMs). A major problem hindering the development of CLMs further, for non-rigid object alignment/tracking, is how to jointly optimize the global warp update across all local search responses. Previous methods have either used general purpose optimizers (e.g., simplex methods) or graph based optimization techniques. Unfortunately, problems exist with both these approaches when applied to CLMs. In this paper, we propose a new approach for optimizing the global warp update in an efcient manner by enforcing convexity at each local patch response surface. Furthermore, we show that the classic Lucas-Kanade approach to gradient descent image alignment can be viewed as a special case of our proposed framework. Finally, we demonstrate that our approach receives improved performance for the task of non-rigid face alignment/trackin...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Yang Wang, Simon Lucey, Jeffrey F. Cohn
• Keywords: Classic Lucas-kanade Approach, Computer Vision, CVPR 2008, Global Warp Update, Local Patch Response, Non-rigid Face, Non-rigid Object Alignment/tracking